Subspace Clustering with Sparsity and Grouping Effect

Abstract EN

Subspace clustering aims to group a set of data from a union of subspaces into the subspace from which it was drawn.

It has become a popular method for recovering the low-dimensional structure underlying high-dimensional dataset.

The state-of-the-art methods construct an affinity matrix based on the self-representation of the dataset and then use a spectral clustering method to obtain the final clustering result.

These methods show that sparsity and grouping effect of the affinity matrix are important in recovering the low-dimensional structure.

In this work, we propose a weighted sparse penalty and a weighted grouping effect penalty in modeling the self-representation of data points.

The experimental results on Extended Yale B, USPS, and Berkeley 500 image segmentation datasets show that the proposed model is more effective than state-of-the-art methods in revealing the subspace structure underlying high-dimensional dataset.